Expression of the FOXP1 Transcription Factor Is Strongly Associated with Inferior Survival in Patients with Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
Abstract Gene expression profiling studies have reported up-regulated mRNA expression of the FOXP1 forkhead transcription factor in response to normal B-cell activation and high expression in a poor prognosis subtype of diffuse large B-cell lymphoma (DLBCL). The purpose of this study was to investigate the prognostic importance of FOXP1 protein expression in an independent series of DLBCL. First, the specificity of our FOXP1 monoclonal antibody was verified by confirming that it did not recognize the closely related FOXP2, FOXP3, or FOXP4 proteins. FOXP1 protein expression was then analyzed by immunohistochemistry using a DLBCL tissue microarray constructed from 101 previously untreated de novo cases from the British Columbia Cancer Agency. FOXP1 expression was scored as either positive (>30% positive nuclei) or negative (<30% positive nuclei). The overall survival curves clearly showed that patients grouped as FOXP1-positive (40%) had a significantly decreased overall survival (P = 0.0001). FOXP1-positive patients had a median overall survival of 1.6 years compared with 12.2 years in FOXP1-negative cases. In addition, FOXP1-positive patients showed a clear trend to earlier progression in comparison to the FOXP1-negative patients. The analysis of FOXP1 expression within low, medium, and high International Prognostic Index groupings found that FOXP1-negative patients had better overall survival within each group indicating that FOXP1 expression has predictive value independent of the International Prognostic Index subgrouping, a finding that was confirmed in multivariate analysis. These initial results suggest that FOXP1 expression may be important in DLBCL pathogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".